Objective The greatest advantage of the Caofeidian Harbor is its deep channel facing the Bohai Bay. The deep channel is a natural port hub for shipping of the Caofeidian Habor. The construction of the Caofeidian Harb...Objective The greatest advantage of the Caofeidian Harbor is its deep channel facing the Bohai Bay. The deep channel is a natural port hub for shipping of the Caofeidian Habor. The construction of the Caofeidian Harbor has impacted the hydrodynamic environment and the sediments movement, which has attracted much attention about the geomorphic evolution, slope stability and the evolution trend after submarine slope destruction. Insight from this study might be significant for the future development of the Caofeidian Habor, including planning, operation and maintenance.展开更多
Based on more than 4000 km 2D seismic data and seismic stratigraphic analysis, we discussed the extent and formation mechanism of the Qiongdongnan deep sea channel. The Qiongdongnan deep sea channel is a large incised...Based on more than 4000 km 2D seismic data and seismic stratigraphic analysis, we discussed the extent and formation mechanism of the Qiongdongnan deep sea channel. The Qiongdongnan deep sea channel is a large incised channel which extends from the east boundary of the Yinggehai Basin, through the whole Qiongdongnan and the Xisha trough, and terminates in the western part of the northwest subbasin of South China Sea. It is more than 570 km long and 4–8 km wide. The chaotic (or continuous) middle (or high) amplitude, middle (or high) continuity seismic facies of the channel reflect the different lithological distribution of the channel. The channel formed as a complex result of global sea level drop during early Pliocene, large scale of sediment supply to the Yinggehai Basin, inversion event of the Red River strike-slip fault, and tilted direction of the Qiongdongnan Basin. The large scale of sediment supply from Red River caused the shelf break of the Yinggehai Basin to move torwards the S and SE direction and developed large scale of prograding wedge from the Miocene, and the inversion of the Red River strike-slip fault induced the sediment slump which formed the Qiongdongnan deep sea channel.展开更多
Natural gas hydrate(NGH) is one of the important clean energy at present and even in the future. The study of its sedimentary environment and minerogenetic condition has long been a hot issue that has received much co...Natural gas hydrate(NGH) is one of the important clean energy at present and even in the future. The study of its sedimentary environment and minerogenetic condition has long been a hot issue that has received much concern from geologists all over the world. China has successfully obtained the samples of NGH in Shenhu and Dongsha sea areas in 2007, 2013 and 2015, respectively. From this, the continental slope north of the South China Sea becomes an important test site for the study of NGH sedimentary genesis and minerogenetic condition. NGH has been found in Shenhu, Dongsha and Qiongdongnan areas within the continental slope north of South China Sea,at different depths of water, with different sedimentary characteristics, gas genesis, and minerogenetic conditions.Using a seismic sedimentology theory, combining seismic facies results of each facies, sedimentary facies and evolution of each area are documented in turn establishing a sedimentary model by considering palaeogeomorphology, sea level change and tectonic movement. The channel system and MTD(Mass Transport Deposition) system among these three areas were compared focusing on the developing position, appearance and controlling factors. Relative location among three areas is firstly defined that Dongsha area in a nearprovenance steep upper slope, Shenhu area in a normal gentle slope and Qiongdongnan area in an awayprovenance flat plain. Besides, their channel systems are classified into erosional, erosional-aggradational and aggradational channel, and MTD systems into headwall domain, translational domain and toe domain.展开更多
<div style="text-align:justify;"> This paper proposes a deep learning-based channel estimation method for orthogonal frequency-division multiplexing (OFDM) systems. The existing OFDM receiver has low e...<div style="text-align:justify;"> This paper proposes a deep learning-based channel estimation method for orthogonal frequency-division multiplexing (OFDM) systems. The existing OFDM receiver has low estimation accuracy when estimating channel state information (CSI) with fewer pilots. To tackle the problem, in this paper, a deep learning model is first trained by the interpolated channel frequency responses (CFRs) and then used to denoise the CFR estimated by least square (LS) estimation. The proposed deep neural network (DNN) can also be trained in a short time because it only learns the CFR and the network structure is simple. According to the simulation results, the performance of the DNN estimator can be compared with the minimum mean-square error (MMSE) estimator. Furthermore, the DNN approach is more robust than conventional methods when fewer pilots are used. In summary, deep learning is a promising tool for channel estimation in wireless communications. </div>展开更多
On the basis of the analysis of field thermogeochemical data along abnormal zones of a thermal stream in the Bukhara-Khiva, oil-and-gas region of the Turan (Tegermen, Chagakul, Shimoly Alat, Beshtepa) was succeeded to...On the basis of the analysis of field thermogeochemical data along abnormal zones of a thermal stream in the Bukhara-Khiva, oil-and-gas region of the Turan (Tegermen, Chagakul, Shimoly Alat, Beshtepa) was succeeded to obtain important data on a deep structure of sites. Data of gas-chemical and geothermal observations show about confinedness of abnormal concentration of methane to zones of the increased values of the temperature field the measured values of temperatures (Tegermen Square and others). On geoelectric section mines 2-D of inversion of the MT-field depth of 4000 m are lower, among very high-resistance the chemogenic and carbonate deposits of the Paleozoic is traced the subvertical carrying-out abnormal zone. This zone is identified as the channel of a deep heat and mass transfer with which hydrocarbon (HC) deposits are connected. It is shown that electro-investigation when using a geophysical complex can and has to become “advancing” at exploration by oil and gas.展开更多
真实场景点云不仅具有点云的空间几何信息,还具有三维物体的颜色信息,现有的网络无法有效利用真实场景的局部特征以及空间几何特征信息,因此提出了一种双通道特征融合的真实场景点云语义分割方法DCFNet(dual-channel feature fusion of ...真实场景点云不仅具有点云的空间几何信息,还具有三维物体的颜色信息,现有的网络无法有效利用真实场景的局部特征以及空间几何特征信息,因此提出了一种双通道特征融合的真实场景点云语义分割方法DCFNet(dual-channel feature fusion of real scene for point cloud semantic segmentation)可用于不同场景下的室内外场景语义分割。更具体地说,为了解决不能充分提取真实场景点云颜色信息的问题,该方法采用上下两个输入通道,通道均采用相同的特征提取网络结构,其中上通道的输入是完整RGB颜色和点云坐标信息,该通道主要关注于复杂物体对象场景特征,下通道仅输入点云坐标信息,该通道主要关注于点云的空间几何特征;在每个通道中为了更好地提取局部与全局信息,改善网络性能,引入了层间融合模块和Transformer通道特征扩充模块;同时,针对现有的三维点云语义分割方法缺乏关注局部特征与全局特征的联系,导致对复杂场景的分割效果不佳的问题,对上下两个通道所提取的特征通过DCFFS(dual-channel feature fusion segmentation)模块进行融合,并对真实场景进行语义分割。对室内复杂场景和大规模室内外场景点云分割基准进行了实验,实验结果表明,提出的DCFNet分割方法在S3DIS Area5室内场景数据集以及STPLS3D室外场景数据集上,平均交并比(MIOU)分别达到71.18%和48.87%,平均准确率(MACC)和整体准确率(OACC)分别达到77.01%与86.91%,实现了真实场景的高精度点云语义分割。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.41276060)
文摘Objective The greatest advantage of the Caofeidian Harbor is its deep channel facing the Bohai Bay. The deep channel is a natural port hub for shipping of the Caofeidian Habor. The construction of the Caofeidian Harbor has impacted the hydrodynamic environment and the sediments movement, which has attracted much attention about the geomorphic evolution, slope stability and the evolution trend after submarine slope destruction. Insight from this study might be significant for the future development of the Caofeidian Habor, including planning, operation and maintenance.
基金Supported by the National High Technology Research and Development Program of China (863 Program, 2006AA09Z349)the National Basic Research Program of China (2007CB411703)
文摘Based on more than 4000 km 2D seismic data and seismic stratigraphic analysis, we discussed the extent and formation mechanism of the Qiongdongnan deep sea channel. The Qiongdongnan deep sea channel is a large incised channel which extends from the east boundary of the Yinggehai Basin, through the whole Qiongdongnan and the Xisha trough, and terminates in the western part of the northwest subbasin of South China Sea. It is more than 570 km long and 4–8 km wide. The chaotic (or continuous) middle (or high) amplitude, middle (or high) continuity seismic facies of the channel reflect the different lithological distribution of the channel. The channel formed as a complex result of global sea level drop during early Pliocene, large scale of sediment supply to the Yinggehai Basin, inversion event of the Red River strike-slip fault, and tilted direction of the Qiongdongnan Basin. The large scale of sediment supply from Red River caused the shelf break of the Yinggehai Basin to move torwards the S and SE direction and developed large scale of prograding wedge from the Miocene, and the inversion of the Red River strike-slip fault induced the sediment slump which formed the Qiongdongnan deep sea channel.
文摘Natural gas hydrate(NGH) is one of the important clean energy at present and even in the future. The study of its sedimentary environment and minerogenetic condition has long been a hot issue that has received much concern from geologists all over the world. China has successfully obtained the samples of NGH in Shenhu and Dongsha sea areas in 2007, 2013 and 2015, respectively. From this, the continental slope north of the South China Sea becomes an important test site for the study of NGH sedimentary genesis and minerogenetic condition. NGH has been found in Shenhu, Dongsha and Qiongdongnan areas within the continental slope north of South China Sea,at different depths of water, with different sedimentary characteristics, gas genesis, and minerogenetic conditions.Using a seismic sedimentology theory, combining seismic facies results of each facies, sedimentary facies and evolution of each area are documented in turn establishing a sedimentary model by considering palaeogeomorphology, sea level change and tectonic movement. The channel system and MTD(Mass Transport Deposition) system among these three areas were compared focusing on the developing position, appearance and controlling factors. Relative location among three areas is firstly defined that Dongsha area in a nearprovenance steep upper slope, Shenhu area in a normal gentle slope and Qiongdongnan area in an awayprovenance flat plain. Besides, their channel systems are classified into erosional, erosional-aggradational and aggradational channel, and MTD systems into headwall domain, translational domain and toe domain.
文摘<div style="text-align:justify;"> This paper proposes a deep learning-based channel estimation method for orthogonal frequency-division multiplexing (OFDM) systems. The existing OFDM receiver has low estimation accuracy when estimating channel state information (CSI) with fewer pilots. To tackle the problem, in this paper, a deep learning model is first trained by the interpolated channel frequency responses (CFRs) and then used to denoise the CFR estimated by least square (LS) estimation. The proposed deep neural network (DNN) can also be trained in a short time because it only learns the CFR and the network structure is simple. According to the simulation results, the performance of the DNN estimator can be compared with the minimum mean-square error (MMSE) estimator. Furthermore, the DNN approach is more robust than conventional methods when fewer pilots are used. In summary, deep learning is a promising tool for channel estimation in wireless communications. </div>
文摘On the basis of the analysis of field thermogeochemical data along abnormal zones of a thermal stream in the Bukhara-Khiva, oil-and-gas region of the Turan (Tegermen, Chagakul, Shimoly Alat, Beshtepa) was succeeded to obtain important data on a deep structure of sites. Data of gas-chemical and geothermal observations show about confinedness of abnormal concentration of methane to zones of the increased values of the temperature field the measured values of temperatures (Tegermen Square and others). On geoelectric section mines 2-D of inversion of the MT-field depth of 4000 m are lower, among very high-resistance the chemogenic and carbonate deposits of the Paleozoic is traced the subvertical carrying-out abnormal zone. This zone is identified as the channel of a deep heat and mass transfer with which hydrocarbon (HC) deposits are connected. It is shown that electro-investigation when using a geophysical complex can and has to become “advancing” at exploration by oil and gas.
文摘真实场景点云不仅具有点云的空间几何信息,还具有三维物体的颜色信息,现有的网络无法有效利用真实场景的局部特征以及空间几何特征信息,因此提出了一种双通道特征融合的真实场景点云语义分割方法DCFNet(dual-channel feature fusion of real scene for point cloud semantic segmentation)可用于不同场景下的室内外场景语义分割。更具体地说,为了解决不能充分提取真实场景点云颜色信息的问题,该方法采用上下两个输入通道,通道均采用相同的特征提取网络结构,其中上通道的输入是完整RGB颜色和点云坐标信息,该通道主要关注于复杂物体对象场景特征,下通道仅输入点云坐标信息,该通道主要关注于点云的空间几何特征;在每个通道中为了更好地提取局部与全局信息,改善网络性能,引入了层间融合模块和Transformer通道特征扩充模块;同时,针对现有的三维点云语义分割方法缺乏关注局部特征与全局特征的联系,导致对复杂场景的分割效果不佳的问题,对上下两个通道所提取的特征通过DCFFS(dual-channel feature fusion segmentation)模块进行融合,并对真实场景进行语义分割。对室内复杂场景和大规模室内外场景点云分割基准进行了实验,实验结果表明,提出的DCFNet分割方法在S3DIS Area5室内场景数据集以及STPLS3D室外场景数据集上,平均交并比(MIOU)分别达到71.18%和48.87%,平均准确率(MACC)和整体准确率(OACC)分别达到77.01%与86.91%,实现了真实场景的高精度点云语义分割。
